This analysis, conducted by Naderi and Jahanian-Najafabadi, focuses on examining current methods in electroencephalography using machine learning in obsessive-compulsive disorder (OCD) diagnosis. Articles were obtained from PubMed, IEEE, Web of Science, and Scopus and filtered. After duplicates were removed and ineligible articles were excluded, there were 11 articles remaining. A modified version of QUADAS-2 was used to qualitatively analyze the articles. Upon analysis, it was found that the majority of the studies were conducted in Asian countries, which limits generalizability, but there are trends of sharing datasets, which suggests an increase in applicability and reproducibility. Additionally, the inconsistency between defining the subtypes and parameters of OCD presents a research gap, and incomplete descriptions of EEG methods, frequency bands used, and preprocessing steps compromises reproducibility and definitive findings. The authors suggest that in order to increase clinical applicability, future studies should ensure compliance with regulatory criteria, incorporate tools such as COBIDAS-MEEG and TRIPOD to improve standardization and reproducibility, incorporate SHAP and LIME to increase quality of model analysis, and more clearly define differentiation between coexisting disorders.